MultiQC Report

General Statistics

Showing ²⁹⁰/₂₉₀ rows and ²⁴/₃₃ columns.

Sample Name	% GC	Ins. size	≥ 1X	≥ 5X	≥ 10X	≥ 30X	≥ 50X	Median cov	Mean cov	% Aligned	M Aligned	M Total reads	Error rate	5'-3' bias	M Aligned	% Assigned	M Assigned	Insert Size	Mean Insert Size	% Aligned	M Aligned	Frag Length	% Aligned	M Aligned	% Aligned	M Aligned	% Aligned	% Trimmed	% Dups	% GC	Length	% Failed	M Seqs
ERR315326_1																													36.6%	50%	101 bp	33%	8.8
ERR315326_2																													32.2%	50%	101 bp	33%	8.8
ERR315326_R1																												9.4%
ERR315326_R1_val.star																									88.7%	7.2
ERR315326_R2																												17.0%
ERR315326_STAR.srt																88.6%	11.7
ERR315327_1																													51.3%	48%	101 bp	25%	6.1
ERR315327_2																													49.9%	48%	101 bp	17%	6.1
ERR315327_R1																												5.5%
ERR315327_R1_val.star																									93.7%	5.7
ERR315327_R2																												7.0%
ERR315327_STAR.srt																90.8%	10.0
ERR315336_1																													42.1%	49%	101 bp	17%	16.4
ERR315336_2																													40.5%	49%	101 bp	17%	16.4
ERR315336_R1																												4.0%
ERR315336_R1_val.star																									92.5%	14.8
ERR315336_R2																												7.1%
ERR315336_STAR.srt																88.3%	25.2
ERR315353_1																													47.1%	49%	101 bp	25%	13.6
ERR315353_2																													39.4%	49%	101 bp	33%	13.6
ERR315353_R1																												8.4%
ERR315353_R1_val.star																									89.4%	11.6
ERR315353_R2																												13.8%
ERR315353_STAR.srt																89.9%	19.9
ERR315361_1																													34.1%	47%	101 bp	25%	7.4
ERR315361_2																													32.1%	47%	101 bp	17%	7.4
ERR315361_R1																												2.7%
ERR315361_R1_val.star																									88.4%	6.3
ERR315361_R2																												5.8%
ERR315361_STAR.srt																88.8%	11.0
ERR315368_1																													31.4%	48%	101 bp	25%	8.7
ERR315368_2																													29.7%	48%	101 bp	25%	8.7
ERR315368_R1																												7.5%
ERR315368_R1_val.star																									88.8%	7.4
ERR315368_R2																												11.9%
ERR315368_STAR.srt																84.3%	11.4
ERR315374_1																													45.6%	48%	101 bp	25%	15.2
ERR315374_2																													44.2%	49%	101 bp	25%	15.2
ERR315374_R1																												5.1%
ERR315374_R1_val.star																									92.3%	13.7
ERR315374_R2																												7.5%
ERR315374_STAR.srt																90.0%	23.8
ERR315377_1																													45.2%	49%	101 bp	25%	15.0
ERR315377_2																													44.4%	49%	101 bp	25%	15.0
ERR315377_R1																												5.1%
ERR315377_R1_val.star																									92.3%	13.6
ERR315377_R2																												7.5%
ERR315377_STAR.srt																90.0%	23.5
ERR315386_1																													30.3%	48%	101 bp	25%	7.5
ERR315386_2																													28.6%	48%	101 bp	25%	7.5
ERR315386_R1																												10.2%
ERR315386_R1_val.star																									90.6%	6.5
ERR315386_R2																												13.5%
ERR315386_STAR.srt																86.0%	10.0
ERR315394_1																													51.2%	48%	101 bp	25%	6.1
ERR315394_2																													50.1%	48%	101 bp	25%	6.1
ERR315394_R1																												5.5%
ERR315394_R1_val.star																									93.7%	5.6
ERR315394_R2																												6.9%
ERR315394_STAR.srt																90.8%	9.9
ERR315399_1																													41.4%	49%	101 bp	17%	16.1
ERR315399_2																													40.4%	49%	101 bp	17%	16.1
ERR315399_R1																												3.9%
ERR315399_R1_val.star																									92.5%	14.6
ERR315399_R2																												7.0%
ERR315399_STAR.srt																88.3%	24.8
ERR315414_1																													61.0%	47%	101 bp	33%	22.8
ERR315414_2																													58.4%	47%	101 bp	33%	22.8
ERR315414_R1																												12.0%
ERR315414_R1_val.star																									93.3%	20.9
ERR315414_R2																												13.2%
ERR315414_STAR.srt																90.6%	37.3
ERR315424_1																													36.5%	50%	101 bp	33%	8.8
ERR315424_2																													28.2%	50%	101 bp	33%	8.8
ERR315424_R1																												9.2%
ERR315424_R1_val.star																									88.4%	7.2
ERR315424_R2																												17.4%
ERR315424_STAR.srt																88.6%	11.7
ERR315432_1																													39.1%	48%	101 bp	33%	17.6
ERR315432_2																													35.3%	48%	101 bp	33%	17.6
ERR315432_R1																												9.5%
ERR315432_R1_val.star																									94.3%	16.1
ERR315432_R2																												12.4%
ERR315432_STAR.srt																90.1%	27.7
ERR315433_1																													31.6%	48%	101 bp	25%	8.7
ERR315433_2																													29.3%	48%	101 bp	25%	8.7
ERR315433_R1																												7.6%
ERR315433_R1_val.star																									88.9%	7.4
ERR315433_R2																												12.4%
ERR315433_STAR.srt																84.3%	11.5
ERR315438_1																													30.6%	48%	101 bp	25%	7.5
ERR315438_2																													28.5%	48%	101 bp	25%	7.5
ERR315438_R1																												10.2%
ERR315438_R1_val.star																									90.6%	6.5
ERR315438_R2																												13.9%
ERR315438_STAR.srt																86.0%	10.0
ERR315439_1																													56.6%	48%	101 bp	50%	6.3
ERR315439_2																													53.4%	48%	101 bp	42%	6.3
ERR315439_R1																												2.8%
ERR315439_R1_val.star																									57.3%	3.4
ERR315439_R2																												7.2%
ERR315439_STAR.srt																88.1%	5.9
ERR315444_1																													56.7%	48%	101 bp	42%	6.4
ERR315444_2																													53.3%	48%	101 bp	42%	6.4
ERR315444_R1																												2.9%
ERR315444_R1_val.star																									57.4%	3.5
ERR315444_R2																												7.5%
ERR315444_STAR.srt																88.1%	6.0
ERR315451_1																													58.7%	48%	101 bp	25%	15.1
ERR315451_2																													57.4%	48%	101 bp	25%	15.1
ERR315451_R1																												6.1%
ERR315451_R1_val.star																									92.5%	13.8
ERR315451_R2																												7.6%
ERR315451_STAR.srt																90.1%	24.2
ERR315455_1																													41.6%	47%	101 bp	25%	24.3
ERR315455_2																													41.9%	47%	101 bp	33%	24.3
ERR315455_R1																												6.0%
ERR315455_R1_val.star																									92.9%	22.0
ERR315455_R2																												8.5%
ERR315455_STAR.srt																87.9%	36.9
ERR315463_1																													58.7%	48%	101 bp	25%	15.4
ERR315463_2																													57.2%	48%	101 bp	25%	15.4
ERR315463_R1																												6.2%
ERR315463_R1_val.star																									92.5%	14.0
ERR315463_R2																												7.6%
ERR315463_STAR.srt																90.1%	24.6
ERR315476_1																													41.4%	48%	101 bp	25%	14.6
ERR315476_2																													40.5%	48%	101 bp	25%	14.6
ERR315476_R1																												5.5%
ERR315476_R1_val.star																									92.3%	13.2
ERR315476_R2																												8.0%
ERR315476_STAR.srt																88.1%	22.2
ERR315477_1																													40.3%	47%	101 bp	25%	28.5
ERR315477_2																													40.2%	47%	101 bp	33%	28.5
ERR315477_R1																												6.5%
ERR315477_R1_val.star																									92.9%	25.7
ERR315477_R2																												9.0%
ERR315477_STAR.srt																85.3%	41.7
ERR315478_1																													41.9%	48%	101 bp	25%	14.9
ERR315478_2																													40.6%	48%	101 bp	25%	14.9
ERR315478_R1																												5.5%
ERR315478_R1_val.star																									92.3%	13.5
ERR315478_R2																												8.1%
ERR315478_STAR.srt																88.1%	22.6
ERR315487_1																													47.0%	49%	101 bp	25%	13.7
ERR315487_2																													44.6%	49%	101 bp	33%	13.7
ERR315487_R1																												8.5%
ERR315487_R1_val.star																									89.7%	11.7
ERR315487_R2																												13.4%
ERR315487_STAR.srt																89.9%	20.1
ERR315490_1																													34.1%	47%	101 bp	25%	7.4
ERR315490_2																													31.8%	47%	101 bp	17%	7.4
ERR315490_R1																												2.8%
ERR315490_R1_val.star																									88.3%	6.4
ERR315490_R2																												6.1%
ERR315490_STAR.srt																88.8%	11.1
ERR315495_1																													40.0%	47%	101 bp	17%	13.1
ERR315495_2																													37.9%	47%	101 bp	17%	13.1
ERR315495_R1																												2.3%
ERR315495_R1_val.star																									88.5%	11.2
ERR315495_R2																												5.4%
ERR315495_STAR.srt																88.6%	19.7
HS002-PE-R00059_BD0U5YACXX.RHM063_CGATGT_L001_R1																												5.2%	58.6%	50%	101 bp	25%	51.9
HS002-PE-R00059_BD0U5YACXX.RHM063_CGATGT_L001_R1_val.star																									90.4%	45.6
HS002-PE-R00059_BD0U5YACXX.RHM063_CGATGT_L001_R1_val_1																						167.9bp	87.4%	44.1					58.2%	50%	97 bp	25%	50.5
HS002-PE-R00059_BD0U5YACXX.RHM063_CGATGT_L001_R2																												7.3%	58.4%	50%	101 bp	33%	51.9
HS002-PE-R00059_BD0U5YACXX.RHM063_CGATGT_L001_R2_val_2																													50.1%	50%	95 bp	25%	50.5
HS002-PE-R00059_BD0U5YACXX.RHM064_CAGATC_L001_R1																												5.0%	56.7%	51%	101 bp	25%	34.0
HS002-PE-R00059_BD0U5YACXX.RHM064_CAGATC_L001_R1_val.star																									90.2%	29.7
HS002-PE-R00059_BD0U5YACXX.RHM064_CAGATC_L001_R1_val_1																						169.3bp	85.3%	28.1					56.5%	51%	97 bp	25%	32.9
HS002-PE-R00059_BD0U5YACXX.RHM064_CAGATC_L001_R2																												7.3%	55.8%	51%	101 bp	42%	34.0
HS002-PE-R00059_BD0U5YACXX.RHM064_CAGATC_L001_R2_val_2																													49.0%	51%	95 bp	17%	32.9
HS002-PE-R00059_BD0U5YACXX.RHM065_GTGAAA_L001_R1																												5.0%	60.7%	50%	101 bp	25%	69.5
HS002-PE-R00059_BD0U5YACXX.RHM065_GTGAAA_L001_R1_val.star																									89.3%	60.2
HS002-PE-R00059_BD0U5YACXX.RHM065_GTGAAA_L001_R1_val_1																						165.3bp	86.2%	58.1					60.6%	50%	97 bp	25%	67.4
HS002-PE-R00059_BD0U5YACXX.RHM065_GTGAAA_L001_R2																												7.2%	60.1%	51%	101 bp	33%	69.5
HS002-PE-R00059_BD0U5YACXX.RHM065_GTGAAA_L001_R2_val_2																													52.3%	50%	95 bp	25%	67.4
HS002-PE-R00059_BD0U5YACXX.RHM066_CGATGT_L002_R1																												5.9%	59.5%	50%	101 bp	25%	60.3
HS002-PE-R00059_BD0U5YACXX.RHM066_CGATGT_L002_R1_val.star																									88.6%	51.4
HS002-PE-R00059_BD0U5YACXX.RHM066_CGATGT_L002_R1_val_1																						167.9bp	83.7%	48.5					59.2%	50%	97 bp	25%	58.0
HS002-PE-R00059_BD0U5YACXX.RHM066_CGATGT_L002_R2																												31.0%	58.7%	51%	101 bp	50%	60.3
HS002-PE-R00059_BD0U5YACXX.RHM066_CGATGT_L002_R2_val_2																													34.1%	50%	71 bp	25%	58.0
HS002-PE-R00059_BD0U5YACXX.RHM067_CAGATC_L002_R1																												7.7%	58.9%	50%	101 bp	25%	74.2
HS002-PE-R00059_BD0U5YACXX.RHM067_CAGATC_L002_R1_val.star																									89.8%	63.0
HS002-PE-R00059_BD0U5YACXX.RHM067_CAGATC_L002_R1_val_1																						169.4bp	84.5%	59.3					58.7%	50%	97 bp	25%	70.2
HS002-PE-R00059_BD0U5YACXX.RHM067_CAGATC_L002_R2																												32.2%	57.9%	51%	101 bp	50%	74.2
HS002-PE-R00059_BD0U5YACXX.RHM067_CAGATC_L002_R2_val_2																													33.5%	50%	71 bp	33%	70.2
HS002-PE-R00059_BD0U5YACXX.RHM068_GTGAAA_L002_R1																												6.4%	57.5%	50%	101 bp	25%	60.2
HS002-PE-R00059_BD0U5YACXX.RHM068_GTGAAA_L002_R1_val.star																									91.5%	52.6
HS002-PE-R00059_BD0U5YACXX.RHM068_GTGAAA_L002_R1_val_1																						179.8bp	84.5%	48.6					57.1%	50%	97 bp	25%	57.5
HS002-PE-R00059_BD0U5YACXX.RHM068_GTGAAA_L002_R2																												31.5%	56.4%	51%	101 bp	50%	60.2
HS002-PE-R00059_BD0U5YACXX.RHM068_GTGAAA_L002_R2_val_2																													31.3%	50%	71 bp	33%	57.5
HS002-PE-R00059_BD0U5YACXX.RHM069_CGATGT_L003_R1																												4.5%	61.6%	51%	101 bp	25%	55.2
HS002-PE-R00059_BD0U5YACXX.RHM069_CGATGT_L003_R1_val.star																									88.5%	47.4
HS002-PE-R00059_BD0U5YACXX.RHM069_CGATGT_L003_R1_val_1																						173.2bp	85.4%	45.7					61.6%	51%	98 bp	25%	53.5
HS002-PE-R00059_BD0U5YACXX.RHM069_CGATGT_L003_R2																												4.7%	61.0%	51%	101 bp	25%	55.2
HS002-PE-R00059_BD0U5YACXX.RHM069_CGATGT_L003_R2_val_2																													61.4%	51%	98 bp	25%	53.5
HS002-PE-R00059_BD0U5YACXX.RHM070_CAGATC_L003_R1																												5.2%	59.7%	51%	101 bp	25%	45.0
HS002-PE-R00059_BD0U5YACXX.RHM070_CAGATC_L003_R1_val.star																									89.0%	38.7
HS002-PE-R00059_BD0U5YACXX.RHM070_CAGATC_L003_R1_val_1																						178.0bp	85.8%	37.2					59.7%	51%	98 bp	25%	43.4
HS002-PE-R00059_BD0U5YACXX.RHM070_CAGATC_L003_R2																												5.5%	58.9%	51%	101 bp	33%	45.0
HS002-PE-R00059_BD0U5YACXX.RHM070_CAGATC_L003_R2_val_2																													59.4%	51%	97 bp	25%	43.4
HS002-PE-R00059_BD0U5YACXX.RHM071_GTGAAA_L003_R1																												4.6%	55.3%	50%	101 bp	25%	53.7
HS002-PE-R00059_BD0U5YACXX.RHM071_GTGAAA_L003_R1_val.star																									87.5%	45.5
HS002-PE-R00059_BD0U5YACXX.RHM071_GTGAAA_L003_R1_val_1																						173.5bp	80.0%	41.6					55.1%	50%	98 bp	25%	52.0
HS002-PE-R00059_BD0U5YACXX.RHM071_GTGAAA_L003_R2																												4.8%	54.7%	50%	101 bp	25%	53.7
HS002-PE-R00059_BD0U5YACXX.RHM071_GTGAAA_L003_R2_val_2																													54.9%	50%	98 bp	25%	52.0
HS002-PE-R00059_BD0U5YACXX.RHM072_CGATGT_L004_R1																												5.6%	55.9%	50%	101 bp	25%	64.3
HS002-PE-R00059_BD0U5YACXX.RHM072_CGATGT_L004_R1_val.star																									91.2%	56.5
HS002-PE-R00059_BD0U5YACXX.RHM072_CGATGT_L004_R1_val_1																						172.7bp	81.0%	50.2					55.6%	49%	97 bp	25%	61.9
HS002-PE-R00059_BD0U5YACXX.RHM072_CGATGT_L004_R2																												5.7%	55.1%	50%	101 bp	25%	64.3
HS002-PE-R00059_BD0U5YACXX.RHM072_CGATGT_L004_R2_val_2																													55.2%	49%	97 bp	25%	61.9
HS002-PE-R00059_BD0U5YACXX.RHM073_CAGATC_L004_R1																												5.7%	49.4%	49%	101 bp	17%	44.9
HS002-PE-R00059_BD0U5YACXX.RHM073_CAGATC_L004_R1_val.star																									93.4%	40.4
HS002-PE-R00059_BD0U5YACXX.RHM073_CAGATC_L004_R1_val_1																						167.8bp	83.0%	35.9					49.1%	49%	97 bp	17%	43.2
HS002-PE-R00059_BD0U5YACXX.RHM073_CAGATC_L004_R2																												5.8%	48.8%	49%	101 bp	17%	44.9
HS002-PE-R00059_BD0U5YACXX.RHM073_CAGATC_L004_R2_val_2																													49.0%	49%	97 bp	17%	43.2
HS002-PE-R00059_BD0U5YACXX.RHM074_GTGAAA_L004_R1																												5.5%	55.2%	49%	101 bp	25%	73.5
HS002-PE-R00059_BD0U5YACXX.RHM074_GTGAAA_L004_R1_val.star																									94.0%	66.5
HS002-PE-R00059_BD0U5YACXX.RHM074_GTGAAA_L004_R1_val_1																						175.7bp	83.4%	59.0					55.0%	49%	97 bp	25%	70.7
HS002-PE-R00059_BD0U5YACXX.RHM074_GTGAAA_L004_R2																												5.7%	54.4%	49%	101 bp	25%	73.5
HS002-PE-R00059_BD0U5YACXX.RHM074_GTGAAA_L004_R2_val_2																													54.6%	49%	97 bp	25%	70.7
HS002-PE-R00059_BD0U5YACXX.RHM075_CGATGT_L005_R1																												4.3%	56.3%	50%	101 bp	25%	62.9
HS002-PE-R00059_BD0U5YACXX.RHM075_CGATGT_L005_R1_val.star																									89.1%	54.4
HS002-PE-R00059_BD0U5YACXX.RHM075_CGATGT_L005_R1_val_1																						165.3bp	81.3%	49.6					56.2%	50%	98 bp	25%	61.1
HS002-PE-R00059_BD0U5YACXX.RHM075_CGATGT_L005_R2																												4.5%	55.8%	50%	101 bp	25%	62.9
HS002-PE-R00059_BD0U5YACXX.RHM075_CGATGT_L005_R2_val_2																													56.0%	50%	98 bp	25%	61.1
HS002-PE-R00059_BD0U5YACXX.RHM076_CAGATC_L005_R1																												6.1%	49.2%	50%	101 bp	25%	36.2
HS002-PE-R00059_BD0U5YACXX.RHM076_CAGATC_L005_R1_val.star																									92.3%	31.8
HS002-PE-R00059_BD0U5YACXX.RHM076_CAGATC_L005_R1_val_1																						169.6bp	84.7%	29.2					48.2%	50%	98 bp	17%	34.5
HS002-PE-R00059_BD0U5YACXX.RHM076_CAGATC_L005_R2																												6.4%	48.5%	50%	101 bp	25%	36.2
HS002-PE-R00059_BD0U5YACXX.RHM076_CAGATC_L005_R2_val_2																													48.1%	50%	97 bp	17%	34.5
KMABT2																											93.5%
RHM063_CGATGT_L001_STAR.srt										100.0%	91.1	91.1	0.00%	0.77	45.5	87.1%	78.0
RHM063_CGATGT_L001_STAR.srt_stats	51%	171	7.8%	2.8%	1.8%	1.0%	0.7%	0.0X	0.0X
RHM063_multistar.nsrtd																		169 bp	232 bp
RHM064_CAGATC_L001_STAR.srt										100.0%	59.3	59.3	0.00%	0.90	29.6	86.5%	50.4
RHM064_CAGATC_L001_STAR.srt_stats	51%	176	6.5%	2.2%	1.4%	0.7%	0.5%	0.0X	0.0X
RHM064_multistar.nsrtd																		169 bp	243 bp
RHM065_GTGAAA_L001_STAR.srt										100.0%	120.3	120.3	0.00%	0.75	60.1	85.2%	101.0
RHM065_GTGAAA_L001_STAR.srt_stats	50%	166	9.5%	3.5%	2.3%	1.2%	0.9%	0.0X	0.0X
RHM065_multistar.nsrtd																		164 bp	221 bp
RHM066_CGATGT_L002_STAR.srt										100.0%	102.7	102.7	0.00%	0.99	51.3	85.3%	86.7
RHM066_CGATGT_L002_STAR.srt_stats	50%	168	9.1%	3.0%	1.9%	1.0%	0.7%	0.0X	0.0X
RHM066_multistar.nsrtd																		164 bp	218 bp
RHM067_CAGATC_L002_STAR.srt										100.0%	125.9	125.9	0.00%	0.95	62.8	84.7%	105.7
RHM067_CAGATC_L002_STAR.srt_stats	50%	167	10.1%	3.4%	2.3%	1.2%	0.8%	0.0X	0.0X
RHM067_multistar.nsrtd																		165 bp	218 bp
RHM068_GTGAAA_L002_STAR.srt										100.0%	105.1	105.1	0.00%	1.05	52.5	84.9%	88.4
RHM068_GTGAAA_L002_STAR.srt_stats	51%	184	9.5%	3.0%	2.0%	1.0%	0.7%	0.0X	0.0X
RHM068_multistar.nsrtd																		178 bp	248 bp
RHM069_CGATGT_L003_STAR.srt										100.0%	94.6	94.6	0.00%	0.87	47.2	86.1%	80.4
RHM069_CGATGT_L003_STAR.srt_stats	51%	176	8.2%	2.9%	1.9%	1.0%	0.7%	0.0X	0.0X
RHM069_multistar.nsrtd																		172 bp	247 bp
RHM070_CAGATC_L003_STAR.srt										100.0%	77.2	77.2	0.00%	0.88	38.6	86.8%	66.2
RHM070_CAGATC_L003_STAR.srt_stats	51%	185	7.1%	2.5%	1.6%	0.8%	0.6%	0.0X	0.0X
RHM070_multistar.nsrtd																		176 bp	262 bp
RHM071_GTGAAA_L003_STAR.srt										100.0%	90.9	90.9	0.00%	0.85	45.4	83.9%	75.1
RHM071_GTGAAA_L003_STAR.srt_stats	50%	174	10.1%	3.4%	2.2%	1.1%	0.8%	0.0X	0.0X
RHM071_multistar.nsrtd																		166 bp	208 bp
RHM072_CGATGT_L004_STAR.srt										100.0%	112.7	112.7	0.00%	0.86	56.3	83.6%	93.0
RHM072_CGATGT_L004_STAR.srt_stats	50%	172	10.8%	3.7%	2.4%	1.3%	0.9%	0.0X	0.0X
RHM072_multistar.nsrtd																		166 bp	208 bp
RHM073_CAGATC_L004_STAR.srt										100.0%	80.6	80.6	0.00%	0.85	40.3	83.7%	66.4
RHM073_CAGATC_L004_STAR.srt_stats	50%	166	9.4%	3.3%	2.1%	1.1%	0.8%	0.0X	0.0X
RHM073_multistar.nsrtd																		165 bp	207 bp
RHM074_GTGAAA_L004_STAR.srt										100.0%	132.7	132.7	0.00%	0.86	66.2	82.9%	108.7
RHM074_GTGAAA_L004_STAR.srt_stats	50%	174	11.7%	4.3%	2.9%	1.5%	1.1%	0.0X	0.0X
RHM074_multistar.nsrtd																		171 bp	224 bp
RHM075_CGATGT_L005_STAR.srt										100.0%	108.6	108.6	0.00%	0.81	54.2	83.5%	89.4
RHM075_CGATGT_L005_STAR.srt_stats	50%	164	10.8%	3.8%	2.5%	1.3%	0.9%	0.0X	0.0X
RHM075_multistar.nsrtd																		163 bp	203 bp
RHM076_CAGATC_L005_STAR.srt										100.0%	63.6	63.6	0.00%	0.78	31.8	84.7%	52.9
RHM076_CAGATC_L005_STAR.srt_stats	51%	168	7.7%	2.7%	1.8%	0.9%	0.6%	0.0X	0.0X
RHM076_multistar.nsrtd																		167 bp	214 bp
bt.all																											91.8%
quants_RHM063																				86.8%	43.8
quants_RHM064																				85.5%	28.1
quants_RHM065																				84.8%	57.2
quants_RHM066																				83.0%	48.2
quants_RHM067																				83.7%	58.7
quants_RHM068																				84.0%	48.3
quants_RHM069																				84.9%	45.4
quants_RHM070																				85.6%	37.2
quants_RHM071																				78.9%	41.0
quants_RHM072																				80.5%	49.9
quants_RHM073																				82.0%	35.5
quants_RHM074																				82.7%	58.5
quants_RHM075																				80.5%	49.1
quants_RHM076																				83.5%	28.8

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Group	Column	Description	ID	Scale
\|\|	QualiMap	% GC	Mean GC content	`avg_gc`	None
\|\|	QualiMap	Ins. size	Median insert size	`median_insert_size`	None
\|\|	QualiMap	≥ 1X	Fraction of genome with at least 1X coverage	`1_x_pc`	None
\|\|	QualiMap	≥ 5X	Fraction of genome with at least 5X coverage	`5_x_pc`	None
\|\|	QualiMap	≥ 10X	Fraction of genome with at least 10X coverage	`10_x_pc`	None
\|\|	QualiMap	≥ 30X	Fraction of genome with at least 30X coverage	`30_x_pc`	None
\|\|	QualiMap	≥ 50X	Fraction of genome with at least 50X coverage	`50_x_pc`	None
\|\|	QualiMap	Median cov	Median coverage	`median_coverage`	None
\|\|	QualiMap	Mean cov	Mean coverage	`mean_coverage`	None
\|\|	QualiMap	% Aligned	% mapped reads	`percentage_aligned`	None
\|\|	QualiMap	M Aligned	Number of mapped reads (millions)	`mapped_reads`	read_count
\|\|	QualiMap	M Total reads	Number of reads (millions)	`total_reads`	read_count
\|\|	QualiMap	Error rate	Alignment error rate. Total edit distance (SAM NM field) over the number of mapped bases	`general_error_rate`	None
\|\|	QualiMap	5'-3' bias	5'-3' bias	`5_3_bias`	None
\|\|	QualiMap	M Aligned	Reads Aligned (millions)	`reads_aligned`	read_count
\|\|	HTSeq Count	% Assigned	% Assigned reads	`percent_assigned`	None
\|\|	HTSeq Count	M Assigned	Assigned Reads (millions)	`assigned`	read_count
\|\|	Picard	Insert Size	Median Insert Size, all read orientations (bp)	`summed_median`	None
\|\|	Picard	Mean Insert Size	Mean Insert Size, all read orientations (bp)	`summed_mean`	None
\|\|	Salmon	% Aligned	% Mapped reads	`percent_mapped`	None
\|\|	Salmon	M Aligned	Mapped reads (millions)	`num_mapped`	read_count
\|\|	Kallisto	Frag Length	Estimated average fragment length	`fragment_length`	None
\|\|	Kallisto	% Aligned	% processed reads that were pseudoaligned	`percent_aligned`	None
\|\|	Kallisto	M Aligned	Pseudoaligned reads (millions)	`pseudoaligned_reads`	read_count
\|\|	STAR	% Aligned	% Uniquely mapped reads	`uniquely_mapped_percent`	None
\|\|	STAR	M Aligned	Uniquely mapped reads (millions)	`uniquely_mapped`	read_count
\|\|	Bowtie 2 / HiSAT2	% Aligned	overall alignment rate	`overall_alignment_rate`	None
\|\|	Cutadapt	% Trimmed	% Total Base Pairs trimmed	`percent_trimmed`	None
\|\|	FastQC	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	FastQC	M Seqs	Total Sequences (millions)	`total_sequences`	read_count

QualiMap

QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

Coverage histogram

Distribution of the number of locations in the reference genome with a given depth of coverage.

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position (Sims et al. 2014).

Bases of a reference sequence (y-axis) are groupped by their depth of coverage (0×, 1×, …, N×) (x-axis). This plot shows the frequency of coverage depths relative to the reference sequence for each read dataset, which provides an indirect measure of the level and variation of coverage depth in the corresponding sequenced sample.

If reads are randomly distributed across the reference sequence, this plot should resemble a Poisson distribution (Lander & Waterman 1988), with a peak indicating approximate depth of coverage, and more uniform coverage depth being reflected in a narrower spread. The optimal level of coverage depth depends on the aims of the experiment, though it should at minimum be sufficiently high to adequately address the biological question; greater uniformity of coverage is generally desirable, because it increases breadth of coverage for a given depth of coverage, allowing equivalent results to be achieved at a lower sequencing depth (Sampson et al. 2011; Sims et al. 2014). However, it is difficult to achieve uniform coverage depth in practice, due to biases introduced during sample preparation (van Dijk et al. 2014), sequencing (Ross et al. 2013) and read mapping (Sims et al. 2014).

This plot may include a small peak for regions of the reference sequence with zero depth of coverage. Such regions may be absent from the given sample (due to a deletion or structural rearrangement), present in the sample but not successfully sequenced (due to bias in sequencing or preparation), or sequenced but not successfully mapped to the reference (due to the choice of mapping algorithm, the presence of repeat sequences, or mismatches caused by variants or sequencing errors). Related factors cause most datasets to contain some unmapped reads (Sims et al. 2014).

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Cumulative genome coverage

Percentage of the reference genome with at least the given depth of coverage.

For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

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Insert size histogram

Distribution of estimated insert sizes of mapped reads.

To overcome limitations in the length of DNA or RNA sequencing reads, many sequencing instruments can produce two or more shorter reads from one longer fragment in which the relative position of reads is approximately known, such as paired-end or mate-pair reads (Mardis 2013). Such techniques can extend the reach of sequencing technology, allowing for more accurate placement of reads (Reinert et al. 2015) and better resolution of repeat regions (Reinert et al. 2015), as well as detection of structural variation (Alkan et al. 2011) and chimeric transcripts (Maher et al. 2009).

All these methods assume that the approximate size of an insert is known. (Insert size can be defined as the length in bases of a sequenced DNA or RNA fragment, excluding technical sequences such as adapters, which are typically removed before alignment.) This plot allows for that assumption to be assessed. With the set of mapped fragments for a given sample, QualiMap groups the fragments by insert size, then plots the frequency of mapped fragments (y-axis) over a range of insert sizes (x-axis). In an ideal case, the distribution of fragment sizes for a sequencing library would culminate in a single peak indicating average insert size, with a narrow spread indicating highly consistent fragment lengths.

QualiMap calculates insert sizes as follows: for each fragment in which every read mapped successfully to the same reference sequence, it extracts the insert size from the TLEN field of the leftmost read (see the Qualimap 2 documentation), where the TLEN (or 'observed Template LENgth') field contains 'the number of bases from the leftmost mapped base to the rightmost mapped base' (SAM format specification). Note that because it is defined in terms of alignment to a reference sequence, the value of the TLEN field may differ from the insert size due to factors such as alignment clipping, alignment errors, or structural variation or splicing in a gap between reads from the same fragment.

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GC content distribution

Each solid line represents the distribution of GC content of mapped reads for a given sample.

GC bias is the difference between the guanine-cytosine content (GC-content) of a set of sequencing reads and the GC-content of the DNA or RNA in the original sample. It is a well-known issue with sequencing systems, and may be introduced by PCR amplification, among other factors (Benjamini & Speed 2012; Ross et al. 2013).

QualiMap calculates the GC-content of individual mapped reads, then groups those reads by their GC-content (1%, 2%, …, 100%), and plots the frequency of mapped reads (y-axis) at each level of GC-content (x-axis). This plot shows the GC-content distribution of mapped reads for each read dataset, which should ideally resemble that of the original sample. It can be useful to display the GC-content distribution of an appropriate reference sequence for comparison, and QualiMap has an option to do this (see the Qualimap 2 documentation).

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Genomic origin of reads

Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

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Gene Coverage Profile

Mean distribution of coverage depth across the length of all mapped transcripts.

There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

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HTSeq Count

HTSeq Count is part of the HTSeq Python package - it takes a file with aligned sequencing reads, plus a list of genomic features and counts how many reads map to each feature.

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Picard

Picard is a set of Java command line tools for manipulating high-throughput sequencing data.

Insert Size

Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

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Salmon

Salmon is a tool for quantifying the expression of transcripts using RNA-seq data.

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Kallisto

Kallisto is a program for quantifying abundances of transcripts from RNA-Seq data.

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STAR

STAR is an ultrafast universal RNA-seq aligner.

Alignment Scores

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Bowtie 2 / HiSAT2

Bowtie 2 and HISAT2 are fast and memory-efficient tools for aligning sequencing reads against a reference genome. Unfortunately both tools have identical log output by default, so it is impossible to distiguish which tool was used.

Paired-end alignments

This plot shows the number of reads aligning to the reference in different ways.

Please note that single mate alignment counts are halved to tally with pair counts properly.

There are 6 possible types of alignment:

PE mapped uniquely: Pair has only one occurence in the reference genome.
PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
PE one mate mapped uniquely: One read of a pair has one occurence.
PE multimapped: Pair has multiple occurence.
PE one mate multimapped: One read of a pair has multiple occurence.
PE neither mate aligned: Pair has no occurence.

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Cutadapt

Cutadapt is a tool to find and remove adapter sequences, primers, poly-Atails and other types of unwanted sequence from your high-throughput sequencing reads.

This plot shows the number of reads with certain lengths of adapter trimmed. Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length. See the cutadapt documentation for more information on how these numbers are generated.

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FastQ Screen

FastQ Screen allows you to screen a library of sequences in FastQ format against a set of sequence databases so you can see if the composition of the library matches with what you expect.

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FastQC

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

Sequence Counts

Sequence counts for each sample. Duplicate read counts are an estimate only.

This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Per Base Sequence Content

The proportion of each base position for which each of the four normal DNA bases has been called.

To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

To see the data as a line plot, as in the original FastQC graph, click on a sample track.

From the FastQC help:

Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

Click a sample row to see a line plot for that dataset.

Rollover for sample name

Position: -

%T: -

%C: -

%A: -

%G: -

Per Sequence GC Content

The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

From the FastQC help:

This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Sequence Length Distribution

The distribution of fragment sizes (read lengths) found. See the FastQC help

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Sequence Duplication Levels

The relative level of duplication found for every sequence.

From the FastQC Help:

In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Overrepresented sequences

The total amount of overrepresented sequences found in each library.

FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as over represented.

Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

From the FastQC Help:

A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

FastQC lists all of the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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Status Checks

Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

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Cluster Flow

Cluster Flow is a simple and flexible bioinformatics pipeline tool.

Pipelines

Information about pipelines is parsed from *.run files.

Showing ¹⁹/₁₉ rows and ⁴/₄ columns.

Pipeline ID	Pipeline Name	Date Started	Genome ID	# Starting Files
cf_fastq_kallisto_1463151720.0	fastq_kallisto	2016-05-13 16:02	GRCh38	4
cf_fastq_kallisto_1463151780.0	fastq_kallisto	2016-05-13 16:03	GRCh38	6
cf_fastq_kallisto_1463213280.0	fastq_kallisto	2016-05-14 09:08	GRCh38	8
cf_fastq_kallisto_1463255280.0	fastq_kallisto	2016-05-14 20:48	GRCh38	10
cf_htseq_counts_1502712271	htseq_counts	2017-08-14 13:04	GRCh38	1
cf_htseq_counts_1502723772	htseq_counts	2017-08-14 16:16	GRCh38	4
cf_htseq_counts_1502723782	htseq_counts	2017-08-14 16:16	GRCh38	3
cf_htseq_counts_1502723788	htseq_counts	2017-08-14 16:16	GRCh38	4
cf_htseq_counts_1502723793	htseq_counts	2017-08-14 16:16	GRCh38	3
cf_htseq_counts_1502723802	htseq_counts	2017-08-14 16:16	GRCh38	3
cf_htseq_counts_1502723811	htseq_counts	2017-08-14 16:16	GRCh38	2
cf_htseq_counts_1502723823	htseq_counts	2017-08-14 16:17	GRCh38	4
cf_htseq_counts_exon_1502799975	htseq_counts_exon	2017-08-15 13:26	GRCh38	16
cf_htseq_counts_exon_1502804457	htseq_counts_exon	2017-08-15 14:40	GRCh38	5
cf_htseq_counts_exon_1502804470	htseq_counts_exon	2017-08-15 14:41	GRCh38	5
cf_htseq_counts_exon_1502804478	htseq_counts_exon	2017-08-15 14:41	GRCh38	4
cf_kallisto_1463247480.0	kallisto	2016-05-14 18:38	GRCh38	10
cf_kallisto_1463255220.0	kallisto	2016-05-14 20:47	GRCh38	8
cf_qualimap_bamqc_1513786946	qualimap_bamqc	2017-12-20 16:22	GRCh38	2

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Group	Column	Description	ID	Scale
\|\|	Cluster Flow	Pipeline Name	Pipeline Name	`pipeline_name`	None
\|\|	Cluster Flow	Date Started	Date and time that pipeline was started (YYYY-MM-DD HH:SS)	`pipeline_start`	None
\|\|	Cluster Flow	Genome ID	ID of reference genome used	`genome`	None
\|\|	Cluster Flow	# Starting Files	Number of input files at start of pipeline run.	`num_starting_files`	None

Pipeline Steps: cf_fastq_kallisto_1463255280.0 (`fastq_kallisto`)

#fastqc
#trim_galore
	#kallisto

Pipeline Steps: cf_fastq_kallisto_1463213280.0 (`fastq_kallisto`)

#fastqc
#trim_galore
	#kallisto

Pipeline Steps: cf_fastq_kallisto_1463151780.0 (`fastq_kallisto`)

#fastqc
#trim_galore
	#kallisto

Pipeline Steps: cf_kallisto_1463247480.0 (`kallisto`)

#kallisto

Pipeline Steps: cf_fastq_kallisto_1463151720.0 (`fastq_kallisto`)

#fastqc
#trim_galore
	#kallisto

Pipeline Steps: cf_kallisto_1463255220.0 (`kallisto`)

#kallisto

Pipeline Steps: cf_htseq_counts_exon_1502804470 (`htseq_counts_exon`)

#htseq_counts_exon

Pipeline Steps: cf_htseq_counts_exon_1502804478 (`htseq_counts_exon`)

#htseq_counts_exon

Pipeline Steps: cf_qualimap_bamqc_1513786946 (`qualimap_bamqc`)

#qualimap_bamqc

Pipeline Steps: cf_htseq_counts_exon_1502804457 (`htseq_counts_exon`)

#htseq_counts_exon

Pipeline Steps: cf_htseq_counts_1502712271 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723788 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723772 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723793 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723823 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723782 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_exon_1502799975 (`htseq_counts_exon`)

#htseq_counts_exon

Pipeline Steps: cf_htseq_counts_1502723811 (`htseq_counts`)

#htseq_counts

Pipeline Steps: cf_htseq_counts_1502723802 (`htseq_counts`)

#htseq_counts

Commands

Every Cluster Flow run will have many different commands. MultiQC splits these by whitespace, collects by the tool name and shows the first command found. Any terms not found in all subsequent calls are replaced with [variable] (typically input and ouput filenames). Each column is for one Cluster Flow run.

Showing ⁶/₆ rows and ²¹/₂₁ columns.

Tool	cf_fastq_kallisto_1463151779	cf_fastq_kallisto_1463213296	cf_fastq_kallisto_1463255307	cf_fastq_screen_1493809678	cf_htseq_counts_1502712083	cf_htseq_counts_1502712271	cf_htseq_counts_1502723772	cf_htseq_counts_1502723782	cf_htseq_counts_1502723788	cf_htseq_counts_1502723793	cf_htseq_counts_1502723802	cf_htseq_counts_1502723811	cf_htseq_counts_1502723823	cf_htseq_counts_exon_1502799768	cf_htseq_counts_exon_1502799975	cf_htseq_counts_exon_1502804457	cf_htseq_counts_exon_1502804470	cf_htseq_counts_exon_1502804478	cf_kallisto_1463247492	cf_kallisto_1463255244	unknown
fastqc	`fastqc -q [variable]`	`fastqc -q [variable]`	`fastqc -q [variable]`
trim_galore	`trim_galore --paired --gzip --phred33 --fastqc [variable] [variable]`	`trim_galore --paired --gzip --phred33 --fastqc [variable] [variable]`	`trim_galore --paired --gzip --phred33 --fastqc [variable] [variable]`
kallisto	`kallisto quant -t 8 --pseudobam -i /home/rsh46/scratch/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.cdna.all.idx -o [variable] -b 100 [variable] [variable] \| samtools view -Sb - > out.bam [variable]`	`kallisto quant -t 1 --pseudobam -i /home/rsh46/scratch/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.cdna.all.idx -o [variable] -b 100 [variable] [variable] \| samtools view -Sb - > out.bam [variable]`	`kallisto quant -t 1 --pseudobam -i /home/rsh46/scratch/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.cdna.all.idx -o [variable] -b 100 [variable] [variable] \| samtools view -Sb - > [variable]`																`kallisto quant -t 1 --pseudobam -i /home/rsh46/scratch/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.cdna.all.idx -o [variable] -b 100 [variable] [variable] \| samtools view -Sb - > [variable]`	`kallisto quant -t 1 --pseudobam -i /home/rsh46/scratch/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.cdna.all.idx -o [variable] -b 100 [variable] [variable] \| samtools view -Sb - > [variable]`
fastq_screen				`fastq_screen --subset 100000 --quiet --aligner bowtie2 --paired [variable] [variable]`
samtools view					`samtools view -h RHM076_CAGATC_L005_STAR.srt.bam \| htseq-count -o RHM076_CAGATC_L005_STAR.srt.bam_annotated.sam -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > RHM076_CAGATC_L005_STAR.srt.bam_counts.txt`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view -h [variable] \| htseq-count -o [variable] -t exon -s no -q -i 'gene_id' - /storage/Genomes/Homo_sapiens/GRCh38/Homo_sapiens.GRCh38.84.gtf \| sort -n -k 2 -r > [variable]`	`samtools view [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] > [variable]`	`samtools view [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] > [variable]`	`samtools view [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] > [variable]`	`samtools view [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] > [variable]`	`samtools view [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] [variable] > [variable]`
qualimap																					`qualimap bamqc -sd -c -nt 4 -bam [variable]`

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Group	Column	Description	ID	Scale
\|\|	Cluster Flow	cf_fastq_kallisto_1463151779	cf_fastq_kallisto_1463151779	`cf_fastq_kallisto_1463151779`	None
\|\|	Cluster Flow	cf_fastq_kallisto_1463213296	cf_fastq_kallisto_1463213296	`cf_fastq_kallisto_1463213296`	None
\|\|	Cluster Flow	cf_fastq_kallisto_1463255307	cf_fastq_kallisto_1463255307	`cf_fastq_kallisto_1463255307`	None
\|\|	Cluster Flow	cf_fastq_screen_1493809678	cf_fastq_screen_1493809678	`cf_fastq_screen_1493809678`	None
\|\|	Cluster Flow	cf_htseq_counts_1502712083	cf_htseq_counts_1502712083	`cf_htseq_counts_1502712083`	None
\|\|	Cluster Flow	cf_htseq_counts_1502712271	cf_htseq_counts_1502712271	`cf_htseq_counts_1502712271`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723772	cf_htseq_counts_1502723772	`cf_htseq_counts_1502723772`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723782	cf_htseq_counts_1502723782	`cf_htseq_counts_1502723782`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723788	cf_htseq_counts_1502723788	`cf_htseq_counts_1502723788`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723793	cf_htseq_counts_1502723793	`cf_htseq_counts_1502723793`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723802	cf_htseq_counts_1502723802	`cf_htseq_counts_1502723802`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723811	cf_htseq_counts_1502723811	`cf_htseq_counts_1502723811`	None
\|\|	Cluster Flow	cf_htseq_counts_1502723823	cf_htseq_counts_1502723823	`cf_htseq_counts_1502723823`	None
\|\|	Cluster Flow	cf_htseq_counts_exon_1502799768	cf_htseq_counts_exon_1502799768	`cf_htseq_counts_exon_1502799768`	None
\|\|	Cluster Flow	cf_htseq_counts_exon_1502799975	cf_htseq_counts_exon_1502799975	`cf_htseq_counts_exon_1502799975`	None
\|\|	Cluster Flow	cf_htseq_counts_exon_1502804457	cf_htseq_counts_exon_1502804457	`cf_htseq_counts_exon_1502804457`	None
\|\|	Cluster Flow	cf_htseq_counts_exon_1502804470	cf_htseq_counts_exon_1502804470	`cf_htseq_counts_exon_1502804470`	None
\|\|	Cluster Flow	cf_htseq_counts_exon_1502804478	cf_htseq_counts_exon_1502804478	`cf_htseq_counts_exon_1502804478`	None
\|\|	Cluster Flow	cf_kallisto_1463247492	cf_kallisto_1463247492	`cf_kallisto_1463247492`	None
\|\|	Cluster Flow	cf_kallisto_1463255244	cf_kallisto_1463255244	`cf_kallisto_1463255244`	None
\|\|	Cluster Flow	unknown	unknown	`unknown`	None

Toggle navigation v1.8

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About MultiQC

General Statistics

General Statistics: Columns

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Paired-end alignments Help

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Rollover for sample name

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Cluster Flow

Pipelines

Cluster Flow Pipelines: Columns

Pipeline Steps: cf_fastq_kallisto_1463255280.0 (fastq_kallisto)

Pipeline Steps: cf_fastq_kallisto_1463213280.0 (fastq_kallisto)

Pipeline Steps: cf_fastq_kallisto_1463151780.0 (fastq_kallisto)

Pipeline Steps: cf_kallisto_1463247480.0 (kallisto)

Pipeline Steps: cf_fastq_kallisto_1463151720.0 (fastq_kallisto)

Pipeline Steps: cf_kallisto_1463255220.0 (kallisto)

Pipeline Steps: cf_htseq_counts_exon_1502804470 (htseq_counts_exon)

Pipeline Steps: cf_htseq_counts_exon_1502804478 (htseq_counts_exon)

Pipeline Steps: cf_qualimap_bamqc_1513786946 (qualimap_bamqc)

Pipeline Steps: cf_htseq_counts_exon_1502804457 (htseq_counts_exon)

Pipeline Steps: cf_htseq_counts_1502712271 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723788 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723772 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723793 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723823 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723782 (htseq_counts)

Pipeline Steps: cf_htseq_counts_exon_1502799975 (htseq_counts_exon)

Pipeline Steps: cf_htseq_counts_1502723811 (htseq_counts)

Pipeline Steps: cf_htseq_counts_1502723802 (htseq_counts)

Commands

Cluster Flow Commands: Columns

v1.8

Highlight Samples

Rename Samples

Show / Hide Samples

Coverage histogram

Cumulative genome coverage

Insert size histogram

GC content distribution

Genomic origin of reads

Gene Coverage Profile

Paired-end alignments

Sequence Counts

Sequence Quality Histograms

Per Sequence Quality Scores

Per Base Sequence Content

Per Sequence GC Content

Per Base N Content

Sequence Duplication Levels

Overrepresented sequences

Adapter Content

Status Checks

Pipeline Steps: cf_fastq_kallisto_1463255280.0 (`fastq_kallisto`)

Pipeline Steps: cf_fastq_kallisto_1463213280.0 (`fastq_kallisto`)

Pipeline Steps: cf_fastq_kallisto_1463151780.0 (`fastq_kallisto`)

Pipeline Steps: cf_kallisto_1463247480.0 (`kallisto`)

Pipeline Steps: cf_fastq_kallisto_1463151720.0 (`fastq_kallisto`)

Pipeline Steps: cf_kallisto_1463255220.0 (`kallisto`)

Pipeline Steps: cf_htseq_counts_exon_1502804470 (`htseq_counts_exon`)

Pipeline Steps: cf_htseq_counts_exon_1502804478 (`htseq_counts_exon`)

Pipeline Steps: cf_qualimap_bamqc_1513786946 (`qualimap_bamqc`)

Pipeline Steps: cf_htseq_counts_exon_1502804457 (`htseq_counts_exon`)

Pipeline Steps: cf_htseq_counts_1502712271 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723788 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723772 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723793 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723823 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723782 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_exon_1502799975 (`htseq_counts_exon`)

Pipeline Steps: cf_htseq_counts_1502723811 (`htseq_counts`)

Pipeline Steps: cf_htseq_counts_1502723802 (`htseq_counts`)